def __init__(self, writeM_readS_lock):
self.writeM_readS_lock = writeM_readS_lock
data = Data()
data.data["output"] = Output()
self.output = data.data["output"]
print("[OK] Sender started")
self.write()
def __init__(self, writeR_readM_lock):
self.writeR_readM_lock = writeR_readM_lock
data = Data()
data.data["input"] = Input()
self.input = data.data["input"]
print("[OK] Receiver started")
self.read()
def __init__(self, writeR_readM_lock,writeM_readS_lock):
self.writeR_readM_lock = writeR_readM_lock
self.writeM_readS_lock = writeM_readS_lock
data = Data()
data.data["input"] = Input()
self.input = data.data["input"]
data.data["output"] = Output()
self.output = data.data["output"]
print("[OK] Main started")
self.main()
def gerenation_df_severity(self, start_col, dataUpload):
d = Data()
list_title = list(d.file_variables_title())
full_set_df = d.data() # dataUpload
new_df = pd.DataFrame()
new_df[list_title[
0]] = full_set_df.iloc[:, start_col] * full_set_df.iloc[:, (
start_col - 1) + 2]
for col in range(len(list_title) - 1):
start_col = start_col + 1
new_df[list_title[
col +
1]] = full_set_df.iloc[:, start_col +
1] * full_set_df.iloc[:, start_col + 2]
return new_df
def generateData(amount, myList):
for i in range(amount):
id = i
name = "User" + str(i)
person = Data(id, name)
myList.append(person)
return myList
def generateData(amount, myList):
for i in range(amount):
id = i
name = names.get_first_name()
person = Data(id, name)
myList.append(person.id)
myList.append(person.name)
myList.append(person.key)
myList.append(person.status)
return myList
def parse_contents(contents, filename, date):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
severity_df = StatisticsController()
try:
if 'csv' in filename:
# Assume that the user uploaded a CSV file
df_X = pd.read_csv(io.StringIO(decoded.decode('utf-8')))
# data.set_df_X(severity_df.get_allData(df_X))
# print(data.get_df_X())
elif 'xls' in filename:
# Assume that the user uploaded an excel file
df_X = pd.read_excel(io.BytesIO(decoded))
data.set_df_X(df_X)
except Exception as e:
print(e)
return html.Div(['There was an error processing this file.'])
return html.Div([
html.H5(filename),
html.H6(datetime.datetime.fromtimestamp(date)),
html.Hr(), # horizontal line
])
import colorlover as cl
import plotly.graph_objs as go
import numpy as np
from sklearn import metrics
import colorlover as cl
import plotly.graph_objs as go
import numpy as np
from collections import OrderedDict
from sklearn import metrics
from Model.Data import Data
from Service.StatisticsService import StatisticsController
data = Data()
df_frec = StatisticsController().generate_statistics(data.df_X)
def serve_prediction_plot(model,
X_train,
X_test,
y_train,
y_test,
Z,
xx,
yy,
mesh_step,
threshold):
# Get train and test score from model
y_pred_train = (model.decision_function(X_train) > threshold).astype(int)
y_pred_test = (model.decision_function(X_test) > threshold).astype(int)
def generation_df_impact_frecuency(self, directory):
d = Data().data()
new_df = pd.DataFrame(d)
return new_df
def title(self):
df = pd.DataFrame(Data().file_variables_title())
return df